WO2015101061A1 - 一种平面地形中地下管状设施红外成像探测定位方法 - Google Patents

一种平面地形中地下管状设施红外成像探测定位方法 Download PDF

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WO2015101061A1
WO2015101061A1 PCT/CN2014/085758 CN2014085758W WO2015101061A1 WO 2015101061 A1 WO2015101061 A1 WO 2015101061A1 CN 2014085758 W CN2014085758 W CN 2014085758W WO 2015101061 A1 WO2015101061 A1 WO 2015101061A1
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image
underground
iteration
underground tubular
target image
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PCT/CN2014/085758
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French (fr)
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张天序
马文绚
鲁岑
郝龙伟
王岳环
桑农
杨卫东
朱虎
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华中科技大学
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Priority to US15/106,703 priority Critical patent/US10365399B2/en
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V8/00—Prospecting or detecting by optical means
    • G01V8/10—Detecting, e.g. by using light barriers
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
    • G01J5/00—Radiation pyrometry, e.g. infrared or optical thermometry
    • G01J5/10—Radiation pyrometry, e.g. infrared or optical thermometry using electric radiation detectors
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V8/00—Prospecting or detecting by optical means
    • G01V8/02—Prospecting
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/70—Determining position or orientation of objects or cameras
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0004—Industrial image inspection

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  • the invention belongs to the field of intersection of geophysics and remote sensing technology, and more particularly relates to an infrared imaging detection and positioning method for underground tubular facilities in a planar terrain.
  • Urban underground pipelines are an important part of urban infrastructure. They are regarded as the “blood vessels” of cities. They are criss-crossed and spread throughout the city, and they are constructed into a densely-woven urban underground pipeline network. The urban underground pipelines are kept at all times. Providing energy, information and water for the city provides a foundation and guarantee for economic development and civic life. Due to the large number of underground pipelines and incomplete data, it has caused great difficulties in urban planning, development and construction, scientific management and correct decision-making. Blind excavation during the construction process caused accidents such as gas pipeline explosion and communication cable being dug, which caused great losses to the country and the people. Underground pipeline inspection is of great significance in archaeological, petroleum, gas, mineral transportation, etc. Therefore, it is necessary to carry out research on detection and identification of tubular underground facilities.
  • infrared imaging has been proposed as a new detection technology for underground tubular facilities, at present, underground imaging facilities using infrared imaging at home and abroad directly use infrared imaging sensors for imaging observation and manual interpretation.
  • infrared remote sensing imaging The physical basis of infrared remote sensing imaging is that a large amount of solar energy is irradiated to the soil to absorb heat, and the heated soil emits infrared radiation that is detected by a thermal infrared sensor.
  • the daily heating and cooling of natural solar energy has different effects on the buried object and the land surrounding it, resulting in a detectable temperature difference.
  • the presence of underground tubular structures can produce flat surface heat distribution anomalies that can be used to detect and locate subsurface targets.
  • the present invention provides an infrared imaging detection and positioning method for an underground tubular facility in a planar terrain, which is modulated by a ground layer of an underground tubular facility according to a Gaussian model of energy diffusion of an underground tubular facility.
  • the original infrared image formed after the inverse modulation process is performed to obtain a target image of the underground tubular facility, and the method includes the following steps:
  • Infrared imaging detection and positioning method for underground tubular facilities in planar terrain characterized in that the method performs inverse modulation processing on the original infrared image formed by the underground tubular device after being modulated by the ground layer according to the Gaussian model of energy diffusion of the underground tubular facility Obtaining a target image of the underground tubular facility, the method comprising the steps of:
  • step (3) It is judged whether the iteration termination condition is satisfied. If it is satisfied, the target image f n obtained by the current iteration is the final target image f; if not, the process returns to step (3), and the iterative calculation is continued.
  • the iteratively solving the thermal expansion function h n and the target image f n using the single-frame image blind deconvolution method based on the Bayesian theory in the step (3) specifically includes:
  • n is the current number of blind iterations
  • f(x) represents the target image
  • g(x) represents the original infrared image
  • h(x) is the thermal diffusion function
  • h(-x) represents the conjugate of h(x)
  • f( -x) represents the conjugate of f(x)
  • * is the convolution operator
  • the iterative termination condition in the step (2) is the number of iteration terminations n>N 0 or the error ⁇ , and the step (4) determines whether the iteration condition is satisfied or not:
  • the method of the invention By inversely modulating the infrared image formed by the underground tubular device after being modulated by the ground layer, the method of the invention not only makes the infrared image display of the original underground tubular facility clearer, but also can invert the real structure of the underground tubular facility. Based on the known infrared information in the obtained infrared image, the key physical characteristics of the underground tubular facility can be inferred, and the purpose of "seeing” and further quantitatively measuring the key physical characteristics of the underground tubular facility can be inferred.
  • Figure 1 is a schematic view showing the overall flow of the present invention
  • Figure 2 is a schematic diagram of thermal diffusion
  • Figure 4 is a cross-sectional view of the underground tubular target
  • Figure 5 is a top view of the thermal diffusion of the underground tubular target
  • Figure 6 is a typical tubular underground target model
  • Fig. 7 is a simulation model of the thermal distribution of the tubular underground target and the temperature difference ⁇ T of the surface and background regions on the functional zone;
  • FIG. 8 is a long-wave infrared image of an upper and a lower water pipeline according to an embodiment of the present invention.
  • Figure 9 is a long-wavelength infrared image of a surface of a tubular underground hot water pipe in an embodiment of the present invention.
  • Figure 10 is a flow chart of a single frame blind image restoration algorithm in the present invention.
  • Figure 12 shows the original infrared image of the underground hot water pipe (top) and its inverse transformation (below).
  • the invention provides an infrared imaging detection and positioning method for an underground tubular facility in a planar terrain.
  • the method performs inverse modulation processing on an original infrared image formed by a ground layer modulation of an underground tubular facility according to a Gaussian model of energy diffusion of an underground tubular facility.
  • the problem of detection is attributed to the problem of inversion in mathematical physics.
  • the structural information and position of the underground tubular facility are detected to realize the detection and location of the underground tubular facility, which has not been seen in the existing domestic and foreign literatures. The same report was invented.
  • the flow of the present invention is shown in FIG. 1.
  • the specific implementation method includes the following steps: thermal infrared characteristic analysis and modeling of a planar underground tubular target, modulation process of an underground tubular target image, inverse tubular image processing of an underground tubular object, and recovery of an underground tubular target image And positioning.
  • thermal infrared imaging remote sensing For underground tubular targets, due to the thermal energy exchange between the distributed heat source of the target and the surrounding formation, and the difference in thermal inertia between the tubular underground facility and the surrounding formation, it can be detected by thermal infrared imaging remote sensing.
  • the underground target to generate thermal infrared radiation anomaly on the surface.
  • thermodynamic point of view we study the influence of the existence of the subsurface target on the regional thermodynamic characteristics, and establish a one-dimensional Gaussian thermal diffusion mathematical model that can accurately describe the regional temperature distribution.
  • the soil environment in which the underground tubular target is located is assumed to be a relatively uniform thermal diffusion environment.
  • the heat source is constant, along the direction perpendicular to the underground tubular target, centering on the underground tubular target, heat is diffused to the formation and its adjacent formation.
  • the thermal diffusion diagram is shown in Figure 2. With the heat source as the center point, the heat spreads to 360° in the surrounding space, and the surface heat is less than 180°. The heat is overlapped along the underground tubular target and cannot be separated.
  • the heat distribution in the vertical and underground tubular target directions conforms to the one-dimensional Gaussian distribution in the state of achieving thermal equilibrium, and the heat distribution model is shown in Fig. 3.
  • ANSYS In order to display the infrared information of the surface of the underground tubular target after the formation, we use ANSYS to simulate the heat distribution of the underground tubular target at a depth of 100 meters in a plane surface environment.
  • the specific modeling steps for thermal diffusion simulation of underground tubular facilities are as follows:
  • the surface effect unit SURF151 which utilizes the node stroke unit of the solid surface and directly covers the surface of the solid element; wherein the SURF151 unit has the main real constant (angular coefficient, Stefan-Boltzmann constant), material properties (density, thermal diffusion) ), surface load (convection, Heat flux density) and body load (heat generation rate);
  • Figure 6 is a typical tubular underground target model.
  • Figure 7 is the thermal distribution of the tubular underground target and the temperature difference ⁇ T simulation model of the surface and background regions on the functional zone.
  • the underground tubular target maintains a substantially constant temperature and humidity state due to various requirements and is different from the formation environment.
  • the heat diffusion existing in the tunnel itself is modulated by the formation, which causes the material and energy migration state of the detection area to change, resulting in a change.
  • the surface temperature distribution produces anomalies and produces a unique infrared information field that is different from other detection zones.
  • the modulation form is expressed as a one-dimensional Gaussian thermal diffusion modulation model perpendicular to the direction of the underground tubular target. We verified this process through experiments. The specific experimental process is as follows:
  • the standard deviation parameter ⁇ of the Gaussian thermal diffusion function may have the following relationship with the buried depth h of the underground building and the thermal conductivity ⁇ of the formation material:
  • the infrared image of the underground tubular target was obtained by thermal imaging, and the thermal halo image was formed after the formation modulation, which was compared by the long-wave infrared image and the long-wave infrared image.
  • the image shows the heat band that appears on the surface of the formation after thermal diffusion of the underground tubular target.
  • Figure 8 shows the long-wave infrared image of the upper and lower water pipes. It is not visible in the infrared image. The approximate location of the upper and lower water pipes is compared with the thermal image.
  • the thermal target image of the underground target (pipeline) modulated by the formation forms the heat band formed on the surface after thermal diffusion.
  • we construct the corresponding energy convergence inverse transform module based on the modeling of the energy modulation through the forward modulation and thermal diffusion of the formation.
  • the type, after inverse transformation, reveals the underground structure information obscured by the stratum.
  • the algorithm can guarantee the maximum recovery of the underground tubular target energy band in the probability sense.
  • Blind deconvolution refers to image restoration techniques in the case where the complete information of the Point Spread Function (PSF) and the target cannot be obtained.
  • Ayers and Dainty proposed a scheme that is essentially a generalization of the Feinup phase recovery algorithm. The method used is iterative and uses prior knowledge to limit the non-negative nature of the image.
  • the estimated values of the target and PSF can be obtained by simple inverse filtering in each iteration.
  • Davey et al. proposed a similar scheme, but their algorithm further set a priori knowledge that the target support rate is known.
  • Wiener filters were used to obtain estimates of the target and point spread functions, thus providing better noise compensation.
  • the Richardson-Lucy algorithm is derived from the Bayesian theorem. Due to the conditional probability, this algorithm takes into account the statistical bias in the signal and thus has the ability to recover noise images. According to the knowledge of statistical probability, Bayes' theorem can be given by:
  • x) is the conditional probability of event y at a given event x.
  • P(x) is the probability of event x
  • y) is the inverse conditional probability, that is, the probability of event x given event y.
  • equation (3) is substituted into equation (4).
  • n is the number of blind iterations
  • Equation (7) It can be known from equation (7) that if an initial value f i,0 , h i,0 and the number of iterations n are given, a corresponding iterative solution can be obtained.
  • the initial estimate of the original image is usually designed as an image space with a pixel value of one, usually by selecting a uniformly distributed initial value according to Bayesian assumptions. This is the famous Richardson-Lucy deconvolution algorithm.
  • n is the number of blind iterations
  • x, z ⁇ X, X is the support domain of the target
  • Y is the support domain of the observed image
  • the support region of the point spread function is generally smaller than the support region of the image.
  • '*' is the convolution operator. It is assumed here that the point spread function h(x) is known, so we can get the target f(x) by iterating over equation (9) until it converges. An initial estimate of the target f 0 (x) is used to initiate this algorithm. Then, in subsequent iterations, due to the form taken by the algorithm, the large deviation from the real target in the initial estimate is quickly discarded in the initial iteration; the details are slower in subsequent iterations. Add it to the ground. If the initial estimate f 0 (x) ⁇ 0, the advantage of this algorithm is that it contains a non-negative constraint, and as the iteration proceeds, the energy is preserved.
  • the point spread function h n (x) is calculated according to a specified value m of the Richardson-Lucy iteration, as shown in equation (10), where the subscript m represents the number of Richardson-Lucy iterations.
  • This equation is essentially the inverse of equation (9) because the target and point spread functions are inverse, and it computes the point spread function from the target.
  • f n (x) is calculated according to the same number of Richardson-Lucy iterations.
  • This step is performed using the point spread function estimated by the complete iteration of equation (10). In this case, the iteration is performed in the conventional form of equation (9), as shown in equation (11) below.
  • the degraded image is again given as g(x) in equations (10) and (11). The cycle repeats as required. Available
  • n is the current number of blind iterations
  • f(x) represents the target image
  • g(x) represents the original infrared image
  • h(x) is the thermal diffusion function
  • h(-x) represents the conjugate of h(x)
  • f( -x) represents the conjugate of f(x)
  • * is the convolution operator
  • the iteration termination condition is set to the number of iteration terminations n>N 0 or error ⁇ . After each blind iteration is completed, it is judged whether
  • Point spread function For the initial estimation, we use the thermal halo image formed by the formation modulation as the initial target. Point spread function The estimation is based on the physics thermal diffusion theory. The Gaussian thermal diffusion model established in the first part is available. The point spread function ⁇ and depth at each point on the underground tubular target are related to the material. When estimating the point spread function, we assume that For a bottom tubular target, there is not much change in depth and material. According to the 6 ⁇ principle, we can estimate a rough based on the width of the thermal radiation energy band. Thermal diffusion function The standard deviation is the width of the thermal radiation energy band divided by six.
  • the inverse modulation of the image after the ground layer modulation is inversely modulated to solve the correctness of the direct thermal performance of the underground tubular target.
  • Fig. 11 is a view showing the surface temperature distribution (upper right) and the inverse transformation result (bottom right) of the underground facility in a typical underground structure shown in Fig. 6 in a flat, depth 100 m environment. The test proves that the inversion can reveal the underground structure information obscured by the stratum.
  • Figure 12 is the original infrared map of the underground hot water pipeline, and the following figure is the result of the inversion.

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Abstract

一种平面地形中地下管状设施红外成像探测定位方法，根据地下管状设施的能量扩散的高斯模型，对地下管状设施经地层调制后形成的原始红外图像进行反调制处理，得到地下管状设施的目标图像，包括：获取地下管状设施经地层调制后所形成的原始红外图像g；根据原始红外图像g，设定高斯热扩散函数的初始值h0；以原始红外图像g作为初始目标图像f0，根据高斯热扩散函数初始值h0，利用基于贝叶斯理论的单帧图像盲目去卷积方法迭代求解热扩展函数hn和目标图像fn；判断是否满足迭代终止条件，如果满足则本次迭代求解得到的目标图像fn为最终目标图像f；若不满足则继续迭代计算。该方法使原来地下管状设施的红外图像显示更清晰，还可以反演地下管状设施的真实结构。

Description

一种平面地形中地下管状设施红外成像探测定位方法 【技术领域】
本发明属于地球物理学与遥感技术交叉的领域，更具体地，涉及一种平面地形中地下管状设施红外成像探测定位方法。
【背景技术】
城市地下管线是城市基础设施的重要组成部分，被人们喻为城市的“血管”，它们纵横交错，遍布整个城市，构建成一张密织的城市地下管线网，城市地下管线每时每刻不停的为城市提供能源、信息和水，为经济发展和市民生活提供了基础和保障。由于地下管线数量众多，资料不全，在城市编制规划、开发建设、科学管理和正确决策中造成极大地困难。施工过程中盲目开挖造成煤气管道爆炸、通讯电缆被挖断等事故屡屡发生，给国家和人民带来了极大地损失。在考古、石油、煤气、矿物运输等等方面，地下管道检测都具有重要意义，因此，有必要开展管状地下设施探测识别的研究。
当然，最普通和直接的对供热的地下管状设施故障进行探测的方法是人工判读，但是，这种方法十分费时且探测不准确，对无故障的定位未见报道。虽然，红外成像作为地下管状设施探测新技术被提出，但目前国内外使用红外成像的地下管状设施探测技术均直接使用红外成像传感器进行成像观测、人工判读。
红外遥感成像的物理基础是大量的太阳光能量照射土壤被吸收产生热量，这些被加热的土壤发出红外辐射被热红外传感器探测。自然太阳能经过每日循环的加热和冷却对埋藏的物体和包围其周围的土地的影响是不同的，从而导致可探测的温差。地下管状设施的存在可以产生平面地表热分布异常，可用于探测、定位地下目标。
但是上述方法局限性在于：1、地下管道的热异常通过其埋入地层的传导调制，到达地表的热分布发生了与地下管道形态有很大差异的变化，表现为热扩散温差大幅降低，热信号微弱。不仅位置发生变化，而且难于发现与定位；2、直接使用红外成像传感进行人工判读困难，无法正确直观的发现地下管状设施。
【发明内容】
针对现有技术的以上缺陷或改进需求，本发明提供了一种平面地形中地下管状设施红外成像探测定位方法,所述方法根据地下管状设施的能量扩散的高斯模型，对地下管状设施经地层调制后所形成的原始红外图像进行反调制处理，得到地下管状设施的目标图像，所述方法包括以下步骤：
一种平面地形中地下管状设施红外成像探测定位方法，其特征在于，所述方法根据地下管状设施的能量扩散的高斯模型，对地下管状设施经地层调制后所形成的原始红外图像进行反调制处理，得到地下管状设施的目标图像，所述方法包括以下步骤：
(1)获取地下管状设施经地层调制后所形成的原始红外图像g；
(2)根据所述原始红外图像g的热辐射能量带的宽度，设定高斯热扩散函数的初始值h0，并设置迭代终止条件；
(3)以所述原始红外图像g作为初始目标图像f0，根据所述高斯热扩散函数的初始值h0，利用基于贝叶斯理论的单帧图像盲目去卷积方法迭代求解热扩展函数hn和目标图像fn；
(4)判断是否满足迭代终止条件，如果满足，则本次迭代求解得到的目标图像fn即为最终的目标图像f；若不满足，则返回步骤(3)，继续迭代计算。
优选地，所述步骤(3)中利用基于贝叶斯理论的单帧图像盲目去卷积方法迭代求解热扩展函数hn和目标图像fn具体包括：
在第n次盲目迭代时首先按照下面两式进行Richardson－Lucy迭代计 算，
Figure PCTCN2014085758-appb-000001
Figure PCTCN2014085758-appb-000002
其中，m表示当前的Richardson－Lucy迭代次数，m＝1□M，M表示总的Richardson－Lucy迭代次数；
经过M次Richardson－Lucy迭代后，得到
Figure PCTCN2014085758-appb-000003
和
Figure PCTCN2014085758-appb-000004
再将
Figure PCTCN2014085758-appb-000005
和
Figure PCTCN2014085758-appb-000006
作为下式中的hn(x)和fn(x)，求解hn+1(x)和fn+1(x)：
Figure PCTCN2014085758-appb-000007
Figure PCTCN2014085758-appb-000008
其中n为当前盲目迭代次数,f(x)表示目标图像,g(x)表示原始红外图像,h(x)是热扩散函数，h(-x)表示h(x)的共轭，f(-x)表示f(x)的共轭，*为卷积运算符。
优选地，所述步骤(2)中的迭代终止条件为迭代终止次数n>N0或误差ε，所述步骤(4)中判断是否满足迭代条件具体为：
判断是否满足|g-hn+1*fn+1|<ε或者n>N0，如果二者中任一个满足，则满足迭代终止条件，否则不满足。
本发明方法通过对地下管状设施经地层调制后所形成的红外图像进行反调制处理后，不仅使原来地下管状设施的红外图像显示更清晰，还可以反演地下管状设施的真实结构。根据所获得的红外图像中已知的红外信息可以推断出地下管状设施关键物理特征，达到“看见”并进一步定量测量出地下管状设施关键物理特征的目的。
【附图说明】
图1本发明总体流程示意图；
图2热扩散示意图；
图3高斯热分布模型；
图4地下管状目标剖面图；
图5地下管状目标热扩散俯视图；
图6一种典型管状地下目标模型；
图7管状地下目标热分布及功能区上地表与背景区域温差ΔT仿真模型图；
图8本发明实施例中一种上、下水管道长波红外图像；
图9本发明实施例中一种管状地下热水管道上地表长波红外图像；
图10本发明中单帧盲目图像复原算法的流程图；
图11(平地，深度100m)地下目标地表温度分布(右上)及逆变换结果(右下)；
图12地下热水管道原始红外图像(上图)和其逆变换结果(下图)。
【具体实施方式】
为了使本发明的目的、技术方案及优点更加清楚明白，以下结合附图及实施例，对本发明进行进一步详细说明。应当理解，此处所描述的具体实施例仅仅用以解释本发明，并不用于限定本发明。此外，下面所描述的本发明各个实施方式中所涉及到的技术特征只要彼此之间未构成冲突就可以相互组合。
本发明提供了一种平面地形中地下管状设施红外成像探测定位方法,所述方法根据地下管状设施的能量扩散的高斯模型，对地下管状设施经地层调制后所形成的原始红外图像进行反调制处理，把探测问题归结为数学物理中的求逆问题，将地下管状设施的结构信息和位置探测出来，实现对地下管状设施的探测和定位，在现有的国内外文献中还没有看到与本发明相同的报道。
本发明流程如图1所示，具体实施方法包括以下步骤：平面地下管状目标热红外特性分析与建模、地下管状目标图像的调制过程、地下管状目标图像反调制处理、地下管状目标图像的恢复和定位。
(1)地下管状目标热红外特性分析与建模
对于地下管状目标而言，由于目标自身具有的分布式热源与周围地层的热能量交换，以及管状地下设施与周围地层的热惯量差异，可通过热红外成像遥感手段探测。在这里我们利用地下目标对地表产生热红外辐射异常，从热力学的角度分析研究了地下目标的存在对区域热力学特性的影响，建立了能够准确描述区域温度分布的一维高斯热扩散数学模型。
我们将地下管状目标所在的土壤环境假设为相对均匀的热扩散环境，假定热源是恒定的，沿着垂直于地下管状目标的方向，以地下管状目标为中心，热量向地层及其附近地层扩散，其热扩散示意图如图2所示，以热源为中心点，热量向周围空间360°扩散，表达在地表热量范围小于180°，而沿着地下管状目标方向，热量相互重叠，无法将其分离出来，垂直与地下管状目标方向热量分布在达到热平衡的状态下符合一维高斯分布，热量分布模型如图3所示。选取一段地下管状目标，其剖面图如图4所示，地下管状目标的埋深在变化，其上方地层材质不同，因此，高斯热扩散的标准差随着地下管状目标走势而变换，具体与深度和地层上方材质有关，粗细均匀的地下管状目标在地层上表现的热图像也可能呈现非等宽的热扩散带，其热分布俯视图如图5所示。
为了将地下管状目标经地层调制后的地表红外信息显示出来，我们利用ANSYS对地下管状目标在平面地表环境下深度为100米时的热分布进行了仿真。地下管状设施热扩散仿真具体的建模的步骤如下：
●选择表面效应单元SURF151，其利用实体表面的节点行程单元，并直接覆盖在实体单元的表面；其中，SURF151单元有主要实常数(角系数、Stefan-Boltzmann常数)、材料属性(密度、热扩散)、表面载荷(对流、 热流密度)和体载荷(生热率)；
●设置表面效应单元对应的材料属性；设置密度为7800，比热为465，辐射率为1；
●设置表面效应单元的实常数；Stefan-Boltzmann常数为5.67×10-8，角系数的形状参数设置为1；
●创建几何模型、划分网格；Keypoint number设置为默认值，Global Element Sizes为3，即可生成有限元模型；
●利用Anasys加载求解；
●查看求解结果，并利用Anasys画出仿真图像，如图6是一种典型管状地下目标模型，图7是用此方法建立的管状地下目标热分布及功能区上地表与背景区域温差ΔT仿真模型图。
(2)地下管状目标图像调制处理
一般的地下管状目标由于各种要求保持大致恒温恒湿的状态且与地层环境有所不同，其本身存在的热扩散经过地层的调制，就会导致探测区域的物质、能量迁移状态发生改变，导致地表温度分布产生异常，并产生不同于其它探测区域的独特的红外信息场。其调制形式表现为垂直于地下管状目标方向的一维高斯热扩散的调制模型。我们通过试验来验证这一过程，具体实验过程如下：
我们对地下管状目标影响热传递进行了仿真研究，表明地下管状目标的存在可以产生地表热分布异常，可用于探测、定位地下目标。同时，不同的目标深度所导致的地表热时-空分布也存在差异。
在面积为150m2的单元，恒温25°条件下，不同深度、不同埋地介质、不同散热功率时，通过能量交换在上覆地表体现出0.5K温差所需时间仿真计算结果如下表所示，表明地层挡不住目标能量交换。
总深度 土壤 土壤+10m厚花岗岩
  50W/m2 100W/m2 50W/m2 100W/m2
30m 73天 49天 79天 54天
65m 158天 107天 171天 117天
100m 243天 164天 272天 187天
以上实验表明管状地下目标的存在可以产生地表热分布异常，不同介质的地层挡不住目标能量交换。同时，高斯热扩散函数标准差参数σ与地下建筑的埋深h和地层材质导热率ε可能存在有如下关系：
(h/ε)＝kσ+l
我们还对某水电网导致的热分布异常进行了试验和分析，通过热成像获取地下管状目标的红外图像，经过地层调制后形成热晕图像，通过对比长波红外图像和长波红外图像伪彩色显示的图像，可以观测到地下管状目标热扩散后在地层表面表现的热量带。
实验过程如图所示，图8为上、下水管道长波红外图像，在红外图中不可见，所标明的只是和热晕图像对比的上、下水管道的大概位置。
依照此方法，我们对另一区域做了地下热水管道探测，如图9所示，是管状地下热水管道上地表长波红外图像。同样我们在可见光图像中不可见的地下热水管道经地层调制后变得略微可见。
实验表明：地下管状目标的存在可以产生地表热分布异常，尽管在可见光图像中由于地层遮蔽这些管网不可见，但在地下目标(管道)经地层调制后的热晕图像中形成了其热扩散后在地表形成的热量带。
(3)地下管状目标图像反调制处理
地下目标(管道)经地层调制后的热晕图像中形成了其热扩散后在地表形成的热量带，我们可以通过反调制处理准确恢复和定位地下管状目标的具体位置、形态特征、下目标结构和运行状态等。在此，我们在对能量经地层正向调制变换热扩散建模的基础上，构建相应的能量收敛逆变换模 型，经逆变换处理，揭示被地层遮蔽的地下结构信息。
为了从经地层调制后的热晕图像中有效地恢复出地下管状目标，我们提出了一种基于贝叶斯理论的单帧图像盲目去卷积方法。地下管状目标热量带恢复问题类似于从模糊图像中恢复图像的问题，这里，我们借用图像复原的方法。图像复原问题之所以困难，一方面是由于模糊过程的千差万别，很难建立统一的数学模型；另一方面是因为噪声的存在，使得图像复原问题不是一个简单的求逆问题而是一个病态求逆问题。单帧图像盲目去卷积算法是图像复原算法中的一个重要方面，该算法只利用单帧图像就可以获得很好的恢复结果。为了应用概率统计方面的有关知识，我们把原始图像、点扩展函数以及退化图像都看作是概率频率的函数，进而应用贝叶斯理论对其分析处理，从而达到恢复图像的目的。该算法能保证在概率意义上极大程度地恢复出地下管状目标能量带。
1方法概述
盲目去卷积专指在无法获取点扩展函数(Point Spread Function，PSF)和目标的完整信息情况下的图像复原技术。Ayers和Dainty提出了一个方案，本质上它是对于Feinup相位恢复算法的推广。所采用的方法是迭代，并用先验知识来对图像的非负性进行限制。在每一次迭代中可以通过简单的逆滤波得到目标和PSF的估计值。Davey等人提出了类似的方案，但他们的算法进一步设定了先验知识，也即目标支持率是已知的。在他们的研究中，使用维纳滤波器来获得目标和点扩展函数的估计，因此可以提供更好的噪声补偿。
我们对基于Richardson－Lucy的盲目去卷积算法进行了实现。在噪声条件下，Richardson－Lucy算法已被证明具有较强的鲁棒性；因此我们认为以其为基础的盲目去卷积算法要优于Ayers－Dainty和Davey－Lane－Bates的算法。实验结果也证实了我们的新算法具有高容噪性。
为了进一步改善这种算法的性能，我们通过对PSF设定函数形式来加 入额外的先验知识。这一方法被认为将可以得到更好的结果，因为未知量由成千上万个像素值减少到了描述PSF的一小部分参数。采用这一方法很可能使盲目去卷积在许多领域找到用武之地，而在这些领域中无法确切知道一个光学系统的异常情况，但却可以用一些自由参数来描述其特征。如此应用的一个例子便是空间望远镜，在这里，时变引力场所引起的镜片的未知偏差使得我们无法获知PSF的确切信息。
2数学模型与算法原理
我们先对Richardson－Lucy去卷积方法作一个简短的介绍，然后介绍这一算法的盲目形式。Richardson－Lucy算法由贝叶斯定理推导而来。由于与条件概率相关，这个算法考虑到了信号中的统计性偏差，因而具有恢复噪声图像的能力。根据统计概率的知识，贝叶斯定理可由下式给出：
Figure PCTCN2014085758-appb-000009
写成离散形式为
Figure PCTCN2014085758-appb-000010
其中P(y|x)是事件y在给定事件x下的条件概率。P(x)是事件x的概率，而P(x|y)是逆条件概率，也就是给定事件y下的事件x的概率。
对于线性移不变系统的图像复原问题，一般我们假定退化图像g、点扩展函数h和原始图像f具有关系：g＝h*f，符号‘*’表示线性卷积。假定g,h,f均为离散概率频率函数，那么g,h,f上的每一点的数值可以认为是事件(假定收集到单位光子为一个事件)在该点上发生的频率数。在计算过程中，通常将f归一化。为了叙述方便起见，我们对一些符号的表示进行约定：gi,j，hi,j，fi,j表示在图像数组中坐标为(i,j)处的值，例如，gi,j表示退化图像数组g在位置(i,j)处的数值；g,h,f若不带下标则表示整个数 组或对整个数组求和的数值结果(如g＝∑gi)。
若给定退化图像g、点扩展函数h，要求找到原始图像f，我们很自然就想到贝叶斯定理，沿用上面提供的符号，给出在事件gk发生的条件下fi发生的概率的表达式：
Figure PCTCN2014085758-appb-000011
其中，i,j∈f(原始图像空间)，k∈g(退化图像空间)，gk指的是g的任意一部分。考虑到所有的gk联合h作用在fi上的独立性，有
Figure PCTCN2014085758-appb-000012
由于P(fi|gk)＝P(figk)/P(gk)，将方程(3)代入方程(4)，得
Figure PCTCN2014085758-appb-000013
我们看到，在方程(5)的右边也含有P(fi)项，这也是我们想要的解，直接求解很困难。在贝叶斯理论的许多应用中，当像P(fi)这项未知时，我们可以采取这样的一个可以接受的策略，即从不太好的情况中选用一个最好的解以及使用P(fi)的估计来获得近似的P(fi|gk)，因此，由方程(5)我们可以得到下面的迭代方程：
Figure PCTCN2014085758-appb-000014
其中，n为盲目迭代次数
显然，P(fi)＝fi/f，P(gk)＝gk/g，又因为图像复原过程是能量守恒的，即有关系：f＝g(总能量即总光子数)，而且P(gk|fi)＝P(hi,k)＝hi,k/h，
Figure PCTCN2014085758-appb-000015
因此方程(6)可写为：
Figure PCTCN2014085758-appb-000016
即
Figure PCTCN2014085758-appb-000017
由方程(7)可知，如果给定一个初始值fi,0、hi,0和迭代次数n，就可以获得相应的迭代解。通常根据贝叶斯假定选择均匀分布的初始值，即可将原始图像的初始估计设计为一个像素值全为1的图像空间。这就是著名的Richardson－Lucy去卷积算法。
Richardson－Lucy算法受到广泛重视的原因在于它对极大似然估计的应用以及在高噪声水平下获得较佳质量的复原图像的显著能力。因此我们假定这一算法的盲目形式也具有同样的特性。而与之相似的一个盲目去卷积算法也由Holmes采用Dempster等人的极大期望算法得到。
3算法实现及其流程图
为了推导叙述方便，我们将方程(7)写成连续积分的形式：
Figure PCTCN2014085758-appb-000018
其中n是盲目迭代次数，x,z∈X，X为目标的支撑域，y∈Y，Y为观察图像的支撑域，点扩展函数的支撑区域一般比图像的支撑区域要小。当假定观测目标的区域为等晕条件时，点扩展函数将会是空间移不变的，仅仅与(y-x)的差值有关，那么方程(8)可以写成卷积形式：
Figure PCTCN2014085758-appb-000019
其中‘*’是卷积运算符。这里假定点扩展函数h(x)是已知的，所以我们可以通过对方程(9)进行迭代直到其收敛来得到目标f(x)。对目标f0(x)的一个初始估计被用来启动此算法。接着，在随后的迭代中，由于该算法 所采取的形式，初始估计中相对于真实目标的较大的偏差在初始的迭代中被迅速地丢弃了；而细节则在随后的迭代中被更缓慢地添加上去。倘若初始估计f0(x)≥0的话，这一算法的优势还在于它包含一个非负约束条件，而且，随着迭代的进行，能量被加以保存。
在实际情况中，点扩展函数h(x)一般都是未知的，那么上面的迭代算法就很难实施，因此，我们不得不采用新的算法策略。为了避免这种情况，我们采用一种新的迭代方法。这种新的迭代方法具有两个迭代步骤，即分别对点扩展函数和目标图像进行迭代计算。在此算法的盲目形式中，这些去卷积步骤有两步是必需的。在第n次盲目迭代时，假设目标由第n－1次迭代得到。随后，点扩展函数hn(x)按照Richardson－Lucy迭代的一个指定值m来进行计算，正如方程(10)中所示，此处下标m表示Richardson－Lucy迭代次数。这一方程实质上是方程(9)的逆形式，因为目标和点扩展函数是相逆的，并且它由目标来计算点扩展函数。然后，fn(x)按照相同的Richardson－Lucy迭代次数来进行计算。这一步采用由方程(10)完全迭代后所估计得出的点扩展函数来进行。在这一情况中，迭代以方程(9)的常规形式来进行，正如下面的方程(11)所示。退化图像在方程(10)和(11)中再次给定为g(x)。循环如要求的那样重复。可得
Figure PCTCN2014085758-appb-000020
Figure PCTCN2014085758-appb-000021
在第n次盲目迭代时首先按照下面两式进行Richardson－Lucy迭代计算，
Figure PCTCN2014085758-appb-000022
Figure PCTCN2014085758-appb-000023
其中，m表示当前的Richardson－Lucy迭代次数，m＝1□M，M表示总的Richardson－Lucy迭代次数；
经过M次Richardson－Lucy迭代后，得到
Figure PCTCN2014085758-appb-000024
和
Figure PCTCN2014085758-appb-000025
再将
Figure PCTCN2014085758-appb-000026
和
Figure PCTCN2014085758-appb-000027
作为下式中的hn(x)和fn(x)，求解hn+1(x)和fn+1(x)：
Figure PCTCN2014085758-appb-000028
Figure PCTCN2014085758-appb-000029
其中n为当前盲目迭代次数,f(x)表示目标图像,g(x)表示原始红外图像,h(x)是热扩散函数，h(-x)表示h(x)的共轭，f(-x)表示f(x)的共轭，*为卷积运算符。
在迭代开始前，需设置迭代终止条件，例如设置迭代终止条件为迭代终止次数n>N0或误差ε，在每次盲目迭代完成后，判断是否满足|g-hn+1*fn+1|<ε或者n>N0，如果二者中任一个满足，则满足迭代终止条件，结束迭代计算，否则不满足，计算进行迭代计算。
以上的方程采用一维形式来表示，它可直接扩展到二维图像形式。先对目标
Figure PCTCN2014085758-appb-000030
和点扩展函数
Figure PCTCN2014085758-appb-000031
进行初始估计，接着如图12中形式的算法循环被执行。在此无需正性约束条件，因为上述方程总会保证为正。这一算法与Holmes的不同，因为在一次盲目迭代中仅有两次Richardson－Lucy迭代：一次是对PSF的估计，一次是对目标的估计。研究发现，如果一次盲目迭代中Richardson－Lucy迭代次数太少，采用这样的迭代方式所得到的仿真图像效果并不好；然而当一次盲目迭代中Richardson－Lucy迭代的次数增加到大约8次时，则实现了好得多的效果。我们给出单帧盲目图像复原算法的流程图如图10所示。
在对目标
Figure PCTCN2014085758-appb-000032
和点扩展函数
Figure PCTCN2014085758-appb-000033
进行初始估计时，我们把经地层调制后形成的热晕图像当做初始的目标
Figure PCTCN2014085758-appb-000034
点扩展函数
Figure PCTCN2014085758-appb-000035
的估计依据物理学热扩散理论，由第一部分建立的高斯热扩散模型可得，地下管状目标上每一处的点扩散函数σ和深度和材质有关，在估计点扩展函数时，这里我们假设对一个底下管状目标而言，深度和材质都没有太大变化。根据6σ原理，我们可以根据热辐射能量带的宽度估计一个粗略的
Figure PCTCN2014085758-appb-000036
即热扩散函数
Figure PCTCN2014085758-appb-000037
的标准差为热辐射能量带的宽度除以6。
(4)地下管状目标图像的恢复与定位
为了说明基于贝叶斯理论的单帧图像盲目去卷积方法在对地层调制后的图像进行反调制求逆，求解出地下管状目标的直接热表现中的正确性，我们进行了如下实验。在此试验中，由于沿着地下管状目标方向的热扩散是相互重叠，无法分离出来的，我们沿着垂直于地下管状目标的方向对地层调制图像进行一维盲卷积，地下管状目标的方向我们借助于地层调制图像大致观察获得。
图11是图6中所示的典型地下结构在平地，深度100m环境下，地下设施地表温度分布(右上)及逆变换结果(右下)。试验证明通过反演可以揭示被地层遮蔽的地下结构信息。
在此基础上，我们利用反演的方法对地下热水管道进行了进一步处理，其试验结果如下：图12上图是地下热水管道原始红外图，下图是其反演的结果。
本实验表明通过反演可以从极其微弱的热晕信号中获得较为准确的热源位置及其结构形状。而不经过此发明，地下管状目标热扩散经地层调制后和周围的环境融为一体分不清，我们通过此发明可以将地下管状目标恢复定位出来。
本领域的技术人员容易理解，以上所述仅为本发明的较佳实施例而已， 并不用以限制本发明，凡在本发明的精神和原则之内所作的任何修改、等同替换和改进等，均应包含在本发明的保护范围之内。

Claims (3)

  1. 一种平面地形中地下管状设施红外成像探测定位方法，其特征在于，所述方法根据地下管状设施的能量扩散的高斯模型，对地下管状设施经地层调制后所形成的原始红外图像进行反调制处理，得到地下管状设施的目标图像，所述方法包括以下步骤：
    (1)获取地下管状设施经地层调制后所形成的原始红外图像g；
    (2)根据所述原始红外图像g的热辐射能量带的宽度，设定高斯热扩散函数的初始值h0，并设置迭代终止条件；
    (3)以所述原始红外图像g作为初始目标图像f0，根据所述高斯热扩散函数的初始值h0，利用基于贝叶斯理论的单帧图像盲目去卷积方法迭代求解热扩展函数hn和目标图像fn；
    (4)判断是否满足迭代终止条件，如果满足，则本次迭代求解得到的目标图像fn即为最终的目标图像f；若不满足，则返回步骤(3)，继续迭代计算。
  2. 如权利要求1所述的方法，其特征在于，所述步骤(3)中利用基于贝叶斯理论的单帧图像盲目去卷积方法迭代求解热扩展函数hn和目标图像fn具体包括：
    在第n次盲目迭代时首先按照下面两式进行Richardson－Lucy迭代计算，
    Figure PCTCN2014085758-appb-100001
    Figure PCTCN2014085758-appb-100002
    其中，m表示当前的Richardson－Lucy迭代次数，m＝1□M，M表示总的Richardson－Lucy迭代次数；
    经过M次Richardson－Lucy迭代后，得到
    Figure PCTCN2014085758-appb-100003
    和
    Figure PCTCN2014085758-appb-100004
    再将
    Figure PCTCN2014085758-appb-100005
    和
    Figure PCTCN2014085758-appb-100006
    作为下式中的hn(x)和fn(x)，求解hn+1(x)和fn+1(x)：
    Figure PCTCN2014085758-appb-100007
    Figure PCTCN2014085758-appb-100008
    其中n为当前盲目迭代次数,f(x)表示目标图像,g(x)表示原始红外图像,h(x)是热扩散函数，h(-x)表示h(x)的共轭，f(-x)表示f(x)的共轭，*为卷积运算符。
  3. 如权利要求2所述的方法，特征在于，所述步骤(2)中的迭代终止条件为迭代终止次数n>N0或误差ε，所述步骤(4)中判断是否满足迭代条件具体为：
    判断是否满足|g-hn+1*fn+1|<ε或者n>N0，如果二者中任一个满足，则满足迭代终止条件，否则不满足。
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